Comparing machine learning classiflcation schemes - a GIS approach

This project examines the effectiveness of two classification schema: Support Vector Machines (SVM), and Artificial Neural Networks (NN) when applied to geographic (i.e. spatial) data. The context for this study is to examine patterns of urbanization in Mahoning County, OH in relation to several independent driving variables of urban development. These independent variables were constructed using Geographic Information Systems (GIS) and were compared to the dependent variable of the spatial locations of urban areas in Mahoning County. The classification techniques were used in conjunction with the GIS-created variables to predict the location of urban areas within Mahoning County. A comparison of the accuracy of the techniques is presented and conclusions drawn concerning which of the variables are the most influential on urban patterns in the region. Lastly, a spatial analysis of the prediction error is performed for each method.